! git log -1 --format="%H"

aa9dbe508f9a5c056061345264537c2844a78a74

import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 0
# The number of cities in the core net
NC = 7
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 14555, C = 8736

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 4920 | is_connectable
  246 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  287 | is_connected_step
10086 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 600.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 600.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 8736 rows, 14555 columns and 45305 nonzeros
Model fingerprint: 0x02ec7352
Model has 7 SOS constraints
Model has 820 general constraints
Variable types: 2460 continuous, 12095 integer (12095 binary)
Coefficient statistics:
  Matrix range     [1e+00, 2e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [7e-01, 4e+01]
Presolve removed 105 rows and 5234 columns
Presolve time: 0.14s
Presolved: 8631 rows, 9321 columns, 45033 nonzeros
Variable types: 2460 continuous, 6861 integer (6861 binary)
Found heuristic solution: objective 138513.73870

Root relaxation: objective 2.824799e+03, 4466 iterations, 0.19 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 2824.79862    0  188 138513.739 2824.79862  98.0%     -    0s
H    0     0                    52549.920520 2824.79862  94.6%     -    0s
H    0     0                    41045.829979 2824.79862  93.1%     -    0s
     0     0 4894.10752    0  278 41045.8300 4894.10752  88.1%     -    0s
H    0     0                    40713.307468 4894.10752  88.0%     -    1s
     0     0 5068.73820    0  350 40713.3075 5068.73820  87.6%     -    1s
     0     0 5079.46033    0  280 40713.3075 5079.46033  87.5%     -    1s
     0     0 5079.46033    0  283 40713.3075 5079.46033  87.5%     -    1s
     0     0 5088.32254    0  478 40713.3075 5088.32254  87.5%     -    1s
H    0     0                    36834.112041 5088.32254  86.2%     -    1s
     0     0 5088.32254    0  414 36834.1120 5088.32254  86.2%     -    1s
     0     0 5090.60156    0  368 36834.1120 5090.60156  86.2%     -    1s
     0     0 5095.94818    0  340 36834.1120 5095.94818  86.2%     -    1s
     0     0 5096.21255    0  335 36834.1120 5096.21255  86.2%     -    1s
     0     0 5104.58306    0  425 36834.1120 5104.58306  86.1%     -    2s
H    0     0                    36828.573287 5104.58306  86.1%     -    2s
     0     0 5105.24241    0  410 36828.5733 5105.24241  86.1%     -    2s
     0     0 5105.53112    0  394 36828.5733 5105.53112  86.1%     -    2s
H    0     0                    36595.215081 5105.53112  86.0%     -    2s
     0     0 5106.08077    0  359 36595.2151 5106.08077  86.0%     -    2s
H    0     0                    36182.221198 5106.08077  85.9%     -    2s
     0     0 5106.68661    0  398 36182.2212 5106.68661  85.9%     -    2s
     0     0 5106.68661    0  271 36182.2212 5106.68661  85.9%     -    2s
H    0     0                    19341.815138 5106.68661  73.6%     -    3s
H    0     0                    19315.609619 5106.70055  73.6%     -    3s
H    0     2                    19288.338536 5106.70055  73.5%     -    3s
     0     2 5106.70055    0  271 19288.3385 5106.70055  73.5%     -    3s
H   31    40                    19268.338536 5361.73650  72.2%   642    4s
H   31    40                    19196.586967 5361.73650  72.1%   642    4s
H   36    40                    18932.254163 5361.73650  71.7%   592    4s
H   70    79                    18912.254163 5361.73650  71.6%   426    4s
H   70    79                    18846.022644 5361.73650  71.5%   426    4s
H  106   115                    18836.022644 5361.73650  71.5%   337    5s
H  106   115                    18721.130854 5361.73650  71.4%   337    5s
H  157   181                    18711.130853 5361.73650  71.3%   261    5s
H  387   390                    18690.925654 5361.73650  71.3%   149    7s
H  391   390                    18631.837651 5361.73650  71.2%   148    7s
H  393   390                    18162.442094 5361.73650  70.5%   148    7s
H  398   405                    17950.716583 5361.73650  70.1%   151    7s
H 1005   973                    17910.782850 5430.46375  69.7%   110    9s
  1368  1325 8021.48859   48  272 17910.7829 5430.46375  69.7%  99.2   10s
H 1432  1342                    17895.597273 5430.46375  69.7%  99.2   10s
  1455  1344 14644.9723  160  216 17895.5973 5430.46375  69.7%  98.9   15s
H 1465  1283                    17875.081294 5430.46375  69.6%  98.2   18s
H 1465  1218                    17865.081294 5430.46375  69.6%  98.2   18s
  1470  1221 16002.2347  167  431 17865.0813 5430.46375  69.6%  97.9   20s
  1480  1228 8416.09583   20  422 17865.0813 5430.46375  69.6%  97.2   25s
H 1480  1166                    17036.228483 5430.46375  68.1%  97.2   25s
H 1486  1111                    17017.900070 5430.46375  68.1%  96.8   28s
H 1486  1055                    17010.620733 5430.46375  68.1%  96.8   28s
H 1487  1005                    16941.601941 5430.46375  67.9%   143   28s
  1488  1006 5430.46375   13  356 16941.6019 5430.46375  67.9%   145   34s
  1490  1009 5535.88266   14  387 16941.6019 5535.88266  67.3%   146   39s
  1494  1011 5588.19645   15  332 16941.6019 5588.19645  67.0%   151   42s
  1509  1021 5815.09801   17  356 16941.6019 5656.31539  66.6%   162   45s
H 1517   976                    16931.601941 5684.13899  66.4%   171   47s
H 1520   927                    16914.903909 5684.13899  66.4%   175   47s
  1543   941 6046.51894   19  324 16914.9039 5684.13899  66.4%   187   50s
H 1554   899                    16844.080058 5684.13899  66.3%   190   51s
  1590   914 6698.14359   21  335 16844.0801 5684.13899  66.3%   206   55s
  1712   977 7037.09115   26  323 16844.0801 5684.13899  66.3%   223   60s
  1853  1036     cutoff   35      16844.0801 5684.13899  66.3%   235   65s
  2000  1096 7364.13141   46  282 16844.0801 5684.13899  66.3%   244   70s
  2269  1218     cutoff   60      16844.0801 5684.13899  66.3%   249   75s
  2540  1255     cutoff   77      16844.0801 5684.13899  66.3%   250   80s
  2872  1344 14956.5495   93  171 16844.0801 5684.13899  66.3%   257   86s
  3125  1355 8271.33251   20  312 16844.0801 6719.20421  60.1%   265   91s
  3273  1388 10396.1436   35  195 16844.0801 6719.20421  60.1%   271   95s
  3577  1492 12489.9871   19  257 16844.0801 7377.17761  56.2%   273  101s
  3766  1518 12650.9773   27  190 16844.0801 7493.11751  55.5%   280  105s
  4118  1664 16709.0349   28  289 16844.0801 7637.96783  54.7%   280  111s
  4429  1929 9593.53467   34  247 16844.0801 7637.96783  54.7%   276  116s
  4688  2045 10213.5361   55  239 16844.0801 7917.00754  53.0%   275  122s
  4879  2175 8154.24925   55  283 16844.0801 7944.83592  52.8%   276  125s
  5193  2392     cutoff   58      16844.0801 7944.83592  52.8%   274  130s
H 5521  2453                    16780.553067 7944.83592  52.7%   272  133s
  5584  2543 15910.9701   90  118 16780.5531 8134.80129  51.5%   272  137s
H 5594  2535                    16741.222923 8134.80129  51.4%   272  137s
  5794  2624 8466.96668   23  314 16741.2229 8278.94069  50.5%   273  140s
  6153  2831     cutoff   29      16741.2229 8278.94069  50.5%   274  146s
  6358  2960 15775.5195   33  268 16741.2229 8395.98631  49.8%   273  150s
  6608  2996 11619.9847   29  267 16741.2229 8395.98631  49.8%   270  158s
H 6645  2996                    16741.222918 8395.98631  49.8%   270  158s
  6694  3120 15958.6522   30  256 16741.2229 8560.39141  48.9%   270  161s
  6916  3237 11031.9269   42  166 16741.2229 8609.03525  48.6%   271  165s
H 7214  3317                    16741.222914 8661.05117  48.3%   274  168s
  7363  3427 10631.0901   45  191 16741.2229 8682.80589  48.1%   277  172s
  7662  3587     cutoff   55      16741.2229 8748.18907  47.7%   277  176s
  7981  3714 16142.6818  106  126 16741.2229 8761.34780  47.7%   277  180s
  8251  3968 11977.6263   26  267 16741.2229 8813.22816  47.4%   279  185s
  8797  4244 9426.85079   26  363 16741.2229 8905.67876  46.8%   279  194s
  9112  4401     cutoff   25      16741.2229 8930.77323  46.7%   280  198s
  9478  4506 10565.4941   42  259 16741.2229 9049.24203  45.9%   283  203s
  9778  4524 13811.3224   45  193 16741.2229 9155.15246  45.3%   284  209s
H 9781  4524                    16741.222894 9155.15246  45.3%   284  209s
  9842  4712 13710.7148   25  341 16741.2229 9167.27794  45.2%   285  215s
 10227  4950 9931.39780   52  334 16741.2229 9223.77059  44.9%   286  221s
 10642  5157 15215.0129   47  151 16741.2229 9302.52570  44.4%   285  243s
H11007  5309                    16741.222891 9324.69402  44.3%   287  248s
H11021  5309                    16741.222885 9341.06791  44.2%   288  248s
 11371  5403 11851.3530   87  288 16741.2229 9403.69584  43.8%   290  254s
 11606  5617 infeasible  122      16741.2229 9464.74911  43.5%   289  261s
H11610  5617                    16741.222875 9464.74911  43.5%   289  261s
 12109  5783 15659.8123   46  324 16741.2229 9537.42730  43.0%   289  267s
 12531  5876 12000.0470   59  248 16741.2229 9696.88360  42.1%   292  274s
 12760  6171 11076.0064   31  266 16741.2229 9759.27877  41.7%   293  280s
H13290  6171                    16741.222807 9778.92730  41.6%   291  280s
 13400  6312 15825.0453   71  153 16741.2228 9805.20077  41.4%   291  288s
H13512  6312                    16741.222793 9807.12183  41.4%   291  288s
 13843  6438 10252.9662   20  304 16741.2228 9912.82056  40.8%   292  295s
H13908  6438                    16741.222781 9912.82056  40.8%   292  295s
H14296  6628                    16741.222761 9995.77943  40.3%   294  302s
H14357  6628                    16741.222757 10001.0452  40.3%   295  302s
H14438  6628                    16741.222721 10001.0452  40.3%   294  302s
 14769  6917 13789.5345   40  369 16741.2227 10055.3551  39.9%   297  312s
H15008  6917                    16741.222695 10097.6574  39.7%   297  312s
 15443  7137 13533.7524   27  265 16741.2227 10154.6849  39.3%   297  319s
 15895  7344 infeasible   50      16741.2227 10173.4657  39.2%   299  328s
H16254  7344                    16741.222649 10188.1571  39.1%   299  328s
 16538  7627 14250.2484   32  208 16741.2226 10225.4652  38.9%   298  336s
 17158  7754 16497.4242   28  233 16741.2226 10302.5841  38.5%   299  346s
 17528  7914     cutoff   36      16741.2226 10318.5321  38.4%   301  356s
 18099  8192     cutoff   33      16741.2226 10406.4902  37.8%   304  366s
 18848  8349 16560.4459   32  324 16741.2226 10488.3491  37.4%   306  376s
 19317  8677     cutoff   43      16741.2226 10532.1670  37.1%   308  494s
 20120  8826 11848.9896   47  246 16741.2226 10593.3452  36.7%   309  505s
 20488  9193 12217.2845   49  305 16741.2226 10653.1892  36.4%   310  518s
 21283  9525 12396.1646   37  209 16741.2226 10710.8928  36.0%   309  531s
 22174  9935 15704.4983   59  197 16741.2226 10738.0137  35.9%   311  543s
 23148 10148 12314.8839   65  245 16741.2226 10833.4075  35.3%   311  556s
 23884 10422 11653.5952   40  289 16741.2226 10869.4083  35.1%   312  569s
 24794 10855     cutoff   33      16741.2226 10955.0521  34.6%   312  583s
 25909 11113 14842.1567   50  180 16741.2226 10994.0293  34.3%   310  595s
 26725 11220     cutoff   30      16741.2226 11063.5132  33.9%   311  600s

Cutting planes:
  Gomory: 32
  Cover: 1453
  MIR: 308
  Flow cover: 841
  GUB cover: 72
  Inf proof: 8
  Zero half: 78
  RLT: 309
  Relax-and-lift: 7

Explored 27061 nodes (8445871 simplex iterations) in 600.01 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 16741.2 16741.2 16741.2 ... 16741.2

Time limit reached
Best objective 1.674122264862e+04, best bound 1.109345534172e+04, gap 33.7357%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	4.5	4.000000e+00	8.500000
1	1	Borås	False	False	0.7	1.610000e+01	16.800000
2	2	Eskilstuna	True	False	1.1	6.890000e+01	70.000000
3	3	Falun	False	False	2.3	5.258016e-13	2.300000
4	4	Gävle	False	False	1.0	3.130000e+01	32.300001
5	5	Göteborg	False	False	5.8	1.163514e-12	5.800000
6	6	Halmstad	False	False	3.4	6.100000e+00	9.500000
7	7	Haparanda	False	False	4.6	0.000000e+00	4.600000
8	8	Helsingborg	False	False	2.2	3.900000e+00	6.100000
9	9	Hudiksvall	False	False	3.9	2.740000e+01	31.299999
10	10	Jönköping	True	False	1.2	4.610000e+01	47.300000
11	11	Kalmar	False	False	4.0	5.608847e-13	4.000000
12	12	Karlskrona	False	False	1.6	1.278977e-13	1.600000
13	13	Karlstad	False	False	0.9	5.044853e-13	0.900000
14	14	Kiruna	False	False	4.0	1.278977e-13	4.000000
15	15	Kristianstad	False	False	3.0	4.618528e-13	3.000000
16	16	Lidköping	False	False	1.2	7.600000e+00	8.800000
17	17	Linköping	True	False	4.1	5.150000e+01	55.600000
18	18	Luleå	False	False	2.0	1.310000e+01	15.100000
19	19	Malmö	False	False	2.6	1.300000e+00	3.900000
20	20	Motala	False	False	2.4	4.013686e-07	2.400000
21	21	Norrköping	True	False	1.7	6.020000e+01	61.900000
22	22	Nyköping	False	False	4.6	3.966553e-07	4.600000
23	23	Sandviken	False	False	4.5	9.094947e-13	4.500000
24	24	Skellefteå	False	False	4.4	1.510000e+01	19.500000
25	25	Skövde	True	False	2.5	1.380000e+01	16.300000
26	26	Stockholm	False	False	7.0	1.205221e-06	7.000001
27	27	Sundsvall	False	False	0.7	2.670000e+01	27.399999
28	28	Trelleborg	False	False	1.3	1.094236e-12	1.300000
29	29	Uddevalla	False	False	4.0	7.538503e-11	4.000000
30	30	Umeå	False	False	3.2	1.950000e+01	22.700000
31	31	Uppsala	False	False	1.9	3.230000e+01	34.200000
32	32	Varberg	False	False	0.8	9.500000e+00	10.300000
33	33	Vetlanda	False	False	2.1	1.090000e+01	13.000000
34	34	Vänersborg	False	False	3.6	4.000000e+00	7.600000
35	35	Västervik	False	False	1.8	5.929479e-12	1.800000
36	36	Västerås	True	False	1.4	1.110000e+02	112.400000
37	37	Växjö	False	False	2.3	4.600000e+00	6.900000
38	38	Örebro	True	True	4.1	1.133000e+02	5.000000
39	39	Örnsköldsvik	False	False	3.1	2.270000e+01	25.799999
40	40	Östersund	False	False	0.9	1.271871e-12	0.900000

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	19.500000	731.139145	111
1	Eskilstuna	Västerås	CORE	70.000000	430.000000	43
2	Skövde	Örebro	CORE	-5.000000	1320.000000	132
3	Gävle	Hudiksvall	SUB	-31.299999	1278.724532	118
4	Norrköping	Nyköping	SUB	-4.600000	74.253949	58
5	Linköping	Norrköping	CORE	55.600000	440.000000	44
6	Karlskrona	Växjö	SUB	1.600000	62.121900	102
7	Boden	Kiruna	SUB	-4.000000	637.912937	291
8	Jönköping	Skövde	CORE	-16.300000	810.000000	81
9	Sundsvall	Östersund	SUB	-0.900000	70.870188	166
10	Eskilstuna	Stockholm	SUB	-7.000000	234.688115	101
11	Halmstad	Helsingborg	SUB	-6.100000	145.447337	79
12	Jönköping	Vetlanda	SUB	-13.000000	225.433574	65
13	Umeå	Örnsköldsvik	SUB	22.699999	849.479930	111
14	Lidköping	Skövde	SUB	8.800000	105.450189	49
15	Karlstad	Örebro	SUB	0.900000	29.229961	77
16	Halmstad	Varberg	SUB	9.500000	167.432227	65
17	Lidköping	Vänersborg	SUB	-7.600000	121.696732	60
18	Uddevalla	Vänersborg	SUB	4.000000	22.172756	21
19	Hudiksvall	Sundsvall	SUB	-27.399999	641.652295	81
20	Linköping	Motala	SUB	-2.400000	37.641812	51
21	Sandviken	Västerås	SUB	4.500000	174.172912	110
22	Borås	Göteborg	SUB	-5.800000	119.727508	71
23	Falun	Västerås	SUB	2.299999	115.327709	128
24	Jönköping	Linköping	CORE	47.300000	1250.000000	125
25	Linköping	Västervik	SUB	-1.800000	64.378849	97
26	Sundsvall	Örnsköldsvik	SUB	-25.799999	1177.683851	127
27	Vetlanda	Växjö	SUB	-6.900000	143.305429	72
28	Borås	Varberg	SUB	-10.300000	260.758783	84
29	Haparanda	Luleå	SUB	4.600000	210.858567	124
30	Luleå	Skellefteå	SUB	15.100000	742.410222	133
31	Kristianstad	Växjö	SUB	3.000000	134.707658	120
32	Uppsala	Västerås	SUB	34.200000	755.020135	78
33	Gävle	Uppsala	SUB	32.300000	484.711112	60
34	Helsingborg	Malmö	SUB	-3.900000	67.318060	60
35	Västerås	Örebro	CORE	112.400000	930.000000	93
36	Borås	Jönköping	SUB	16.800000	404.484606	82
37	Boden	Luleå	SUB	8.500000	58.656837	32
38	Malmö	Trelleborg	SUB	-1.300000	18.150077	34
39	Eskilstuna	Norrköping	CORE	-61.900000	1020.000000	102
40	Kalmar	Vetlanda	SUB	4.000000	174.202753	119

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  6200.000
subnet = 10541.223
------------------
total  = 16741.223

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))